AI and the workforce

AI is reshaping work across the Belgian banking sector at an unprecedented speed. Where many organizations were still exploring the fundamentals of AI only 12 months ago, the discussion today has become far more concrete: what does AI mean for the workforce, and how must work itself change?

In this context, the primary challenge in today’s labor market shifts from managing talent supply to actively shaping workforce relevance. As organizations rethink how work is designed and delivered, they must take on a more active role as architects of the future of work.

Insights from our qualitative study - conducted in cooperation with Febelfin in May 2026 and involving HR leadership from major banks in Belgium - reinforce this sense of urgency. 75% of HR leaders participating in the study expect a high workforce impact from AI and automation over the next three to five years.

From capability building to structural workforce challenges

As Belgian banks respond to the initial wave of AI-driven transformation, it is becoming clear that while technology can be deployed rapidly, organizations do not always move at the same pace. Indeed, the six Belgian banks are, at best, only at the beginning of the workforce planning journey when compared with peers in other markets. Against this backdrop, seven key challenges linked to the AI-driven workforce transformation emerge and are discussed in more detail below. These challenges have been clustered based on the level of control perceived by HR leaders in our study.

In discussions on AI’s impact on the workforce, training in AI skills is seen as integral to the solution. Belgian banks emphasize that this does not automatically imply a proportional increase in L&D budgets. Instead, they expect a shift away from broad learning catalogues towards more personalized and role-specific learning pathways. In an AI-driven world of work, the focus is moving towards a balanced mix of (1) human-centric soft-skills, (2) AI literacy, and (3) continuously evolving job-specific capabilities that adapt alongside changing roles and business contexts. As reskilling is required, this transition is often accompanied by anxieties about potential job loss, as many face employment uncertainty. Through strategic workforce planning, organizations can anticipate the evolving job landscape within the organization, aimed at maintaining employability in the long term for their workforce. When it comes to governance, risk management, and data protection, HR leaders indicate the banks are putting in place clear policies and practical guidance on the usage of AI.

Information transparency, ethical guidelines, productivity, and performance are perceived as being in motion and only partially within the organization’s control. When it comes to ethical guidelines, a key consideration is how to address the growing concerns around the ecological footprint of AI solutions and ensure that technological innovation remains aligned with organizational values. A key productivity challenge is how to position junior employees in an AI-driven environment: while they remain essential for capability building and maintaining a future talent pipeline, some organizations are simultaneously considering reducing entry-level roles due to AI-enabled productivity gains.

Well-being, leadership, and strategy in the context of AI, are perceived as the most complex considerations. Their impact is more diffuse and less tangible, resulting in a higher degree of uncertainty. When it comes to well-being, the participating HR leadership points to a growing psychological tension between fear and opportunity. Questions arise around what happens when AI removes “easy cognitive work” and leaves employees with consistently higher intensity tasks. These tensions also place pressure on leadership and strategy. Leaders are expected to respond to increasing competitive pressure from fintechs and new market entrants. At the same time, they are expected to guide workforce transformation in a way that preserves engagement and human touch.

The AI exposure x workforce adaptability framework

Most of the banks anticipate cutting jobs, particularly in administrative roles. At the same time, as certain jobs disappear, tasks will shift and new roles will emerge. In this regard, banks refer to the large-scale digital evolution and transformation they have already been undergoing over the past 10-15 years, which ultimately did not result in a massive reduction in the number of jobs but rather in a status quo marked by a clear shift in skills.

Two related but distinct concepts are essential in this debate: AI exposure and adaptability (cf. Figure). The key insight from the AI Exposure × Adaptability lens is that AI exposure does not equal displacement. Instead, it highlights where work will need to be fundamentally redesigned and where targeted interventions, such as reskilling, will be most critical to maintain a workforce that is future proof.

Focusing on adaptability, we identify three levels. Roles with low AI adaptability are at risk as they are typically more operational. When these roles are exposed to AI and workload deflates, the options for the job holders to move to other roles within the organization are limited. Our qualitative study indicates that - according to participants’ assessments - roles such as KYC specialists, retail bankers, and insurance sales advisors fall into this risk category. Targeted reskilling initiatives and clear governance frameworks are viewed as important mechanisms to mitigate displacement risks.

The majority of the roles are estimated to have medium adaptability. These are roles that can adapt, but not without intentional redesign and support. Employees taking on these roles, such as Branch managers and accountants, should be supported through

AI enablement, targeted upskilling for higher-value tasks, accompanied by strong change management.

The high adaptability cluster consists of roles where AI creates a transformation opportunity by accelerating and capturing value, while enabling talent mobility through skill marketplaces. According to participants in the study, roles such as data analysts and internal auditors are expected to face high AI exposure - significantly reducing their workload - but they retain stronger transition opportunities within the organization due to their adaptable analytical capabilities. For workforce planning, this implies a need to proactively redeploy these profiles, accelerating internal mobility while redefining role scopes to focus on higher-value analytical and decisionsupport activities.

Architects of work

For organizations, this implies a more active role: they must become architects of work, deliberately shaping how tasks are redistributed, how roles evolve, and how employees transition between them. Three dimensions are critical to enable this shift: mindset, incentives, and controls.

In terms of mindset, HR leadership of the Belgian banks see opportunities to develop a strong leadership mindset to support transformation, by developing a clear AI narrative, equipping leaders with stronger change-management capabilities, and fostering a constructive culture while keeping the human impact visible. One of the key strategic questions to address relates to how organizations should capture and manage AI-driven productivity gains. Possible scenarios range from a sharp decline in the number of jobs, to alternative work models, or other forms of labor reorganization (such as the four-day work week).

To enable the AI shift, specific incentives are considered and already put in place to accelerate the AI transformation. The banks prefer a collective approach for incentivizing appropriate AI use, e.g. through a CLA 90 bonus, setting collective objectives related to AI training or reskilling. Other concrete actions range from creating communities around AI and making successful use cases visible, to organizing innovation workshops and challenges that reward early adopters.

Controls need to be in place to ensure responsible AI adoption, such as defined tool usage, supported by ethical guidelines and mandatory training on responsible AI use. The underlying intent is to enable experimentation in a safe environment, particularly important given concerns around HR and customer data. A recurring action theme is also the need to ensure employees understand when AI-generated outputs require human validation, especially where junior learning-by-doing is at risk.

Our study shows that AI is accelerating the transformation of work across the Belgian banking sector. However, maturity levels in workforce planning remain limited compared to other markets. The difference between organizations that succeed and those that fall behind will increasingly depend on their ability to deliberately redesign work, balancing productivity gains with workforce sustainability and maintaining engagement throughout periods of change. Long-term success will rely on creating an environment in which innovation and responsible AI adoption can coexist, while ensuring that employees remain equipped, supported, and engaged. As AI reshapes tasks, roles, and organizational structures, workforce adaptability will become a defining factor of competitiveness in the future Belgian banking landscape.